1. Field of the Invention
Methods and apparatuses consistent with the present invention relate to editing an optimized color preference, and more particularly, to editing an optimized color preference of an input image by educating a user about color preference patterns by using a neural network when correcting the color information of a color preference area.
2. Description of the Related Art
Digital devices that reproduce color, such as a monitor, a scanner, a printer and others have diversified their functions and enhanced their quality so as to satisfy various requests of users, and are using different color spaces or color models, depending on the field each device is used in. Color models are divided into a device-dependent model and a device-independent model. The device-dependent models include the RGB model, which is an additive color space model, and the CMYK color model, which is a subtractive color space model. And the device-independent models include the CIE L*a*b model, CIE XYZ model, CIE LUV model, and others. For example, the CMYK color space is used in the printing field, and the RGB color space is used in computer monitors.
Further, color preference refers to colors stochastically having a high preference in a color space. The color preference greatly influences the image output performance of the printer or the display device. Therefore, many inventions for editing and correcting the color preference have been disclosed.
However, color transformation appropriate for an individual preference of a user is difficult because the related art inventions provide general color preference transformation functions, and it takes significant time for color preferences to be edited by providing a preference area on a predefined color space to a user, which are problems.